1. Field of the Invention
The present invention relates particularly to an image recognition apparatus and a method suitable for identifying an object with high accuracy.
2. Description of the Related Art
Conventionally, there is known a face recognition technique for identifying an individual by extracting a face region from an image including a face of a person as a face image, and comparing the extracted face image with a face image of a specific person registered in advance. This face recognition technique is used for security, such as allowing entrance into a room when a person captured by a camera is recognized as a registered person, for example.
On the other hand, there is a desire to perform, using the face recognition technique, image search for a photograph showing the same person. Regarding the security use, recognition with high accuracy is enabled by imposing restrictions on the conditions for capturing a person, but with the case of image search, conditions for capturing a person are varied, and there is a problem that the recognition accuracy is low. For example, even if the same person is shown in photographs, the person may be erroneously determined to be different persons depending on the orientations or expressions of the faces, or the illumination at the time of capturing.
Accordingly, a method for registering a plurality of face images is discussed as a method for performing recognition with high accuracy even when the conditions for capturing the face images are different. For example, Japanese Patent Application Laid-Open No. 11-175718 discusses a method for creating partial spaces from a plurality of face images, and performing recognition based on the similarity between the partial spaces of a registered face and an input face.
On the other hand, Support Vector Data Description (SVDD) is known as a method for performing representation by using a smallest hypersphere containing samples of a class to which a target belongs from a plurality of samples of an identification target (for example, see “Support Vector Data Description” by D. Tax and R. Duin, Machine Learning, 54(1):45-66, 2004). Also, “Support Vector Data Description for Image Categorization From Internet Images” by X. Yu, D. DeMenthon and D. Doermann, 19th International Conference on Pattern Recognition, 2008 discusses a classification method for general object categories using SVDD.
However, according to the method for performing face recognition using partial spaces as discussed in Japanese Patent Application Laid-Open No. 11-175718, a plurality of face images has to be input, and the amount of processing is increased to that extent. Also, even if a face image of the same person as the registered face image is input, the persons may not be identified as the same person. This is because if the expanse of the partial spaces of the registered face image and the input face image are large, the similarity between the partial spaces will not be necessarily high. Also, erroneous recognition is likely to occur due to overlapping of partial spaces caused by an increase in the number of classes. Such characteristics are particularly likely when the number of samples for the registered face image is small.
On the other hand, when the method discussed in “Support Vector Data Description for Image Categorization From Internet Images” described above is applied to face recognition, the problems described above can be overcome because a sample near an identification boundary of a registered face image is extracted as a support vector. However, if SVDD is simply applied, although whether an input sample is of a target category can be identified, the reliability belonging to the category cannot be obtained. Accordingly, when a plurality of people who resemble each other are registered, the most similar person cannot be output as the identification result.